A CT scanner generally includes an x-ray tube mounted on a rotatable gantry opposite a detector array across an examination region. The rotatable gantry and hence the x-ray tube rotate around the examination region. The x-ray tube emits radiation that traverses the examination region and is detected by the detector array. The detector array generates and outputs a signal indicative of a strength of the detected radiation. The signal is reconstructed to generate volumetric image data.
For reading, a clinician has viewed the volumetric image data using different visualization software applications. One such application includes an image segmentation tool which allows the clinician to segment tissue of interest in the volumetric image data for viewing and/or analysis. The segmentation can be performed manually by a user through the image segmentation tool and/or semi-automatically through a combination of acts by the clinician and the image segmentation tool.
With one example semi-automated image segmentation, a user hovers a mouse pointer over a location on a displayed image to identify a location as a point of interest. The actual location of point of interest is used as a seed or starting point for an automated segmentation. The automated segmentation is performed in the background with the segmentation results immediately shown in a viewport superimposed over the image. This is repeated automatically if the mouse pointer is moved over the image for each location identified by the mouse pointer without requiring any mouse-clicks.
Unfortunately, segmentation performed on the volumetric image data being observed may produce a less than optimal result. For instance, a scan performed with an imaging protocol that smooths the data may render it difficult for the clinician and/or visualization software application to accurately identify a perimeter of tissue of interest. In view of at least the above, there is an unresolved need for other approaches for segmenting tissue of interest.